Senior Data Engineer (Senior Engineer II) - Integrations [Remote in the US]

To apply for the Senior Data Engineer (Senior Software Engineer II) position at Rockerbox, candidates should have a minimum of 7 years of experience as a data engineer, with extensive expertise in working with AWS services, particularly Redshift and Kubernetes. A strong background in Python, experience with ingesting data from third-party APIs, and familiarity with modern orchestration tools like Airflow are highly desirable. Additionally, we value mentorship experience and technical project leadership skills.

As a Senior Data Engineer at Rockerbox, you'll be responsible for building and maintaining scalable and resilient data ingestion and pipeline systems. You'll lead projects ensuring efficient data processing while collaborating with cross-functional teams to design solutions that meet business requirements. Your role will also involve mentoring other engineers and participating in design reviews to share knowledge within the team.

Absolutely! The Senior Data Engineer position at Rockerbox is remote-first, which means you can work from anywhere in the United States. We promote a flexible working environment that supports work-life balance, enabling you to thrive in your professional and personal life.

Rockerbox offers a competitive package for its Senior Data Engineers, including a base salary of $175,000, equity options, health, vision, and dental insurance, as well as unlimited PTO. Additional benefits include 10 paid holidays, a company-wide shutdown during the last week of the year, comprehensive parental leave, and a $1,000 annual training stipend to support your continuous learning.

In the role of Senior Data Engineer at Rockerbox, you'll work on mission-critical data pipelines that collect and process marketing data from third-party APIs. Your projects will involve building scalable systems that provide customers with valuable data insights while ensuring data integrity and efficient delivery, directly impacting our clients' ability to make informed decisions.

The culture at Rockerbox is vibrant and supportive. We value teamwork and enjoy a low-ego environment where everyone pitches in to make the company a great place to work. Our team members often showcase their personalities, fostering a fun atmosphere filled with witty banter while also focusing on collaboration and continued improvement.

Your expertise in AWS, particularly with services like Redshift, is essential for the Senior Data Engineer role at Rockerbox. You'll leverage this experience to build and optimize data pipelines that efficiently handle large volumes of marketing data. Understanding AWS allows you to create scalable solutions, improve data availability, and drive efficient data processing, ultimately contributing to our clients' successful marketing strategies.

In responding, focus on detailing the pipeline architecture, the technologies used, such as AWS and Python, and how it addressed specific business needs. Highlight metrics that reflect its efficiency, reliability, and the impact it had on data accuracy and timeliness for decision-making.

While answering, emphasize the importance of fostering a collaborative learning environment. Discuss strategies like code reviews, pair programming, and hosting workshops to enable knowledge sharing. Illustrate how you adapt your mentorship style to fit individual learning preferences and project needs.

Discuss methodologies you employ during the design phase such as load balancing, using microservices, and leveraging cloud resources. Explain how you monitor the pipelines to ensure they perform effectively under varying loads and the tools you utilize for performance assessment.

Focus on the importance of data validation techniques. Describe your approach to identifying discrepancies, implementing automated checks, and employing logging mechanisms. Share examples of how you’ve successfully resolved data anomalies in the past.

Highlight your commitment to maintaining clear and detailed documentation throughout the development cycle. Discuss tools you use for documentation, how it aids in team onboarding, and ensures maintainability for future projects.

Provide insights into how you’ve deployed and managed data services in a Kubernetes environment, discussing benefits like scalability, load balancing, and resource management. Illustrate how Kubernetes contributes to the overall efficiency of your data processing workflows.

Talk about the importance of Infrastructure as Code (IaC) in maintaining consistent environments. Explain tools like Terraform you’ve used and provide examples of how IaC has helped automate deployments and improve the reliability of your data pipelines.

Provide an example of a project where you worked with various teams, such as product management and QA. Focus on communication strategies and tools used to keep everyone aligned, and the overall outcome of the collaboration.

Share your proactive approach to staying informed about the latest trends in data engineering by attending webinars, reading industry publications, and participating in networking events. Highlight specific technologies or methodologies you’ve recently explored and how they could benefit Rockerbox’s objectives.

In answering, mention KPIs like data pipeline throughput, error rates, data latency, and scalability. Explain why these metrics are crucial for evaluating the effectiveness of data processes and how they help in continuous improvement efforts.